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Phytohormone Involvement in the Ustilago maydis– Zea mays Pathosystem: Relationships between Abscisic Acid and Cytokinin Levels and Strain Virulence in Infected Cob Tissue

2015· article· en· W795619785 on OpenAlexafffund
Erin N. Morrison, R. J. Neil Emery, Barry Saville

Bibliographic record

VenuePLoS ONE · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Disease Resistance and Genetics
Canadian institutionsTrent University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsUstilagoBiologyVirulenceAbscisic acidSmutMicrobiologyBotanyGeneticsGene

Abstract

fetched live from OpenAlex

Ustilago maydis is the causative agent of common smut of corn. Early studies noted its ability to synthesize phytohormones and, more recently these growth promoting substances were confirmed as cytokinins (CKs). Cytokinins comprise a group of phytohormones commonly associated with actively dividing tissues. Lab analyses identified variation in virulence between U. maydis dikaryon and solopathogen infections of corn cob tissue. Samples from infected cob tissue were taken at sequential time points post infection and biochemical profiling was performed using high performance liquid chromatography-electrospray ionization tandem mass spectrometry (HPLC-ESI MS/MS). This hormone profiling revealed that there were altered levels of ABA and major CKs, with a marked reduction in CK glucosides, increases in methylthiol CKs and a particularly dramatic increase in cisZ CK forms, in U. maydis infected tissue. These changes were more pronounced in the more virulent dikaryon relative to the solopathogenic strain suggesting a role for cytokinins in moderating virulence during biotrophic infection. These findings highlight the fact that U. maydis does not simply mimic a fertilized seed but instead reprograms the host tissue. Results underscore the suitability of the Ustilago maydis- Zea mays model as a basis for investigating the control of phytohormone dynamics during biotrophic infection of plants.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.185
Threshold uncertainty score0.235

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.153
GPT teacher head0.247
Teacher spread0.094 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations76
Published2015
Admission routes2
Has abstractyes

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